Systems and methods for dynamic wireless network configuration based on mobile radio unit location

ABSTRACT

A system described herein may provide for the use of modeling techniques to generate models associated with various locations or regions (e.g., sectors) associated with one or more radio access networks (“RANs”) of a wireless network, as well as for one or more mobile radio units (“MRUs”) that include wireless network infrastructure and which may be dynamically moved from one location to another. The system may identify attributes of a location within the network at which the MRU is located, and may automatically configure base stations of the RAN as well as the wireless network infrastructure of the MRU in order to optimize the operation of the RAN, as well as to gain optimal usage of the MRU.

BACKGROUND

Wireless networks, such as Long-Term Evolution (“LTE”) networks, FifthGeneration (“5G”) networks, or the like, may include radio accessnetworks (“RANs”), via which user equipment (“UE”), such as mobiletelephones or other wireless communication devices, may receive wirelessservice. Some RANs may be implemented or augmented by vehicle-mounted orother types of mobile RAN infrastructure devices, which may providewireless coverage in varying locations.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A and 1B illustrate an example overview of one or moreembodiments described herein;

FIG. 2 illustrates example interference models, sector models, and/orconfiguration parameters that may be generated, received, maintained,provided, etc. by a mobile radio unit (“MRU”) of some embodiments;

FIG. 3 illustrates example attributes associated with sector models andMRU models, and the location-based association of sector and MRUparameters with sector models and MRU models, in accordance with someembodiments;

FIG. 4 illustrates an example of one or more sector models associatedwith a given sector associated with a RAN of a wireless network, inaccordance with some embodiments;

FIG. 5 illustrates an example process for configuring elements of a RANand an MRU based on sector models, MRU models, and a location of theMRU, in accordance with some embodiments;

FIG. 6 illustrates an example environment in which one or moreembodiments, described herein, may be implemented;

FIG. 7 illustrates an example arrangement of a radio access network(“RAN”), in accordance with some embodiments;

FIG. 8 illustrates an example arrangement of an Open RAN (“O-RAN”)environment in which one or more embodiments, described herein, may beimplemented; and

FIG. 9 illustrates example components of one or more devices, inaccordance with one or more embodiments described herein.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

The following detailed description refers to the accompanying drawings.The same reference numbers in different drawings may identify the sameor similar elements.

Embodiments described herein provide for the use of artificialintelligence/machine learning (“AI/ML”) techniques or other suitabletechniques to model attributes, characteristics, key performanceindicators (“KPIs”), and/or other information associated with variouslocations or regions associated with one or more RANs of a wirelessnetwork (e.g., a Long-Term Evolution (“LTE”) network, a Fifth Generation(“5G”) network, and/or another type of network). As discussed herein,such locations or regions may be referred to as “sectors.” Further, inthe examples discussed herein, sectors may include evenly distributedareas of a uniform shape (e.g., a hexagon). In practice, sectors may bearranged or defined differently. For example, in some embodiments,sectors may be defined with respect to the location of one or more basestations of a RAN (e.g., where a sector may be defined based on acoverage area of the one or more base stations and/or may be definedbased on a physical location at which one or more antennas or otherphysical equipment of the base stations are installed), and/or may bedefined independently of the location of the one or more base stations.

Embodiments described herein further provide for the use of AI/MLtechniques or other suitable techniques to determine configurationparameters of a RAN (e.g., configuration parameters of one or more basestations of the RAN) based on the presence or availability of a mobileradio unit (“MRU”) at a particular geographical location associated withthe RAN (e.g., within, adjacent to, and/or proximate to one or moresectors of the RAN). Embodiments described herein further provide forthe use of AI/ML techniques or other suitable techniques to determineconfiguration parameters of the MRU based on attributes, configurationparameters, KPIs, etc. of sectors of the RAN in which the MRU islocated, to which the MRU is adjacent, and/or to which the MRU isproximate (e.g., within a threshold proximity).

An MRU of some embodiments may include, for example, a vehicle-mounted(or otherwise mobile) base station, radio unit (“RU”), and/or otherwireless infrastructure. In some embodiments, the MRU may include and/ormay be communicatively coupled to a Multi-Access/Mobile Edge Computing(“MEC”) device, referred to sometimes herein simply as a “MEC,” whichmay be co-located with the MRU (e.g., affixed to a same vehicle or otherapparatus that facilitates mobility of the MRU).

Configuration parameters of the RAN and/or the MRU that may be adjustedin accordance with embodiments described herein may include parameterssuch as beamforming parameters (e.g., azimuth angle, beam width, and/orbeam power associated with one or more beams associated with the RAN orthe MRU), carrier aggregation parameters (e.g., whether to use carrieraggregation, bands and/or frequencies on which carrier aggregationshould be used, etc.), routing parameters (e.g., traffic may be routedto a MEC associated with the MRU in lieu of to a MEC associated with aparticular base station and/or to a core of the wireless network,backhaul traffic may be routed via the MRU in lieu of some otherbackhaul link, etc.), mobility parameters (e.g., the MRU may be includedin a neighbor cell list (“NCL”) used by one or more base stations of theRAN, the MRU may be indicated as a “preferred” cell for handovers to orfrom one or more base stations of the RAN, etc.), and/or other suitableparameters as described herein.

The configuration of the MRU based on attributes of the RAN, asdescribed herein, may generally be geared towards improving aspects ofthe RAN (e.g., sectors of the RAN that are within a coverage areaassociated with the MRU and/or sectors of the RAN that are proximate tothe coverage area associated with the MRU). For example, embodimentsdescribed herein may determine, based on RAN KPIs, configurationparameters, etc. that performance metrics associated with the RAN arebelow a threshold level, such as a maximum latency being exceeded or aminimum throughput not being met. In such a scenario, the MRU may beconfigured to improve latency or throughput, such as by serving as arelay node between base stations, serving as a priority or primary cell(e.g., where User Equipment (“UEs”) are instructed to connect to the MRUin lieu of other base stations that are in range of the UEs), performingcarrier aggregation, or the like. As another example, embodimentsdescribed herein may determine that a MEC associated with a particularbase station is overloaded (e.g., utilization of processing resources orother resources of the MEC may exceed a threshold level of utilization),and may configure a MEC associated with the MRU to offload or handlesome or all traffic in lieu of the MEC of the particular base station.Other examples of the configuration of the MRU based on attributes ofthe RAN are provided below.

Similarly, configuration parameters of the RAN may be adjusted based onthe presence of the MRU at a given geographical location associated withthe RAN. For example, beamforming parameters associated with basestations of the RAN may be modified to point away from a coverage areaassociated with the MRU when the MRU is located within a given sector ofthe RAN. As another example, routing parameters of the RAN may bemodified, such as MEC traffic routing parameters, backhaul trafficrouting parameters, etc. As yet another example, bands or frequenciesused by base stations of the RAN may be selected or modified based onbands or frequencies used by the MRU (e.g., to avoid collisions,interference, etc.). Other examples of the configuration of the RANbased on attributes and/or location of the MRU are provided below.

Since embodiments described herein include the determination andperformance of actions in a dynamic manner that is based on theparticular attributes, performance metrics, etc. of sectors in which anMRU is located, the overall performance of the RAN may be enhanced.Further, as described herein, the association between particular sectormodels, interference models, and/or associated configuration parametersmay be generated and/or refined using one or more AI/ML techniques orother suitable techniques (e.g., deep learning, reinforced orunreinforced machine learning, neural networks, K-means clustering,regression analysis, and/or other suitable techniques). In this manner,the particular configuration parameters, performance metrics, etc. of asector may be taken into account when selecting configuration parametersof the MRU and/or modified configuration parameters of the RAN. Further,some or all of the operations may be performed automatically, thuseliminating the need for manual configuration of the RAN and/or MRU. Assuch, the MRU may be positioned at any location within the coveragearea(s) of a RAN and may improve the operation of the RAN in a mannerthat is optimally suited to the particular conditions or demands of theRAN at or near such location.

As shown in FIG. 1A, for example, geographical area (or region) 100 maybe subdivided into a set of sectors 101. The set of sectors 101 mayinclude, as shown, sector 101-1, 101-2, and one or more additionalsectors that are not explicitly illustrated with a reference numeral.Further in this example, each sector 101 may be associated with discretenetwork infrastructure elements, such as particular base stations 103.For example, base station 103-1 may be located in one particular sector101, while base station 103-2 may be located in another sector 101.Further, additional base stations 103 (e.g., base stations notexplicitly illustrated with a reference numeral) may be present ingeographical region 100. That is, the location of each base station 103may be within a particular geographical area (e.g., a hexagonal-shapedgeographical area, in this example) that corresponds to a respectivesector 101. Some sectors 101 may include at least one base station 103,while other sectors 101 may not include any base stations 103.

In the example discussed herein, MRU 105 may be located within sector101-3. As discussed above, MRU 105 may include one or more networkinfrastructure elements, such as one or more RUs, base stations 103(and/or one or more components of a base station 103), MECs, or othernetwork infrastructure elements. MRU 105 may include, and/or may beaffixed, mounted, etc. to, one or more vehicles, such as a truck, adrone, or the like. Additionally, or alternatively, MRU 105 may be ormay include network infrastructure elements that are movable anddeployable without any additional mobility apparatus, vehicles, or thelike.

MRU 105 may be associated with coverage area 107, which may be an areain which radio frequency (“RF”) signals emitted by networkinfrastructure elements (e.g., a base station, a RU, an antenna, etc.)of MRU 105 may reach, and/or may be measured with at least a thresholdamount of received radio power. MRU coverage area 107 is shown as acircle in this example; in practice, MRU coverage area 107 may beanother shape or set of shapes, and/or may be defined in some othersuitable manner. In some embodiments, MRU coverage area 107 may bedefined based on a radius or diameter of a circle, with MRU 105 as acenter point of the circle.

As shown, Global Optimization System (“GOS”) 109 may receive (at 102)KPIs and/or attributes associated with one or more sectors 101. The KPIsmay include RF metrics, such as measures of signal quality, signalstrength, interference, or the like, at given sectors 101 and/orlocations within sectors 101. Such measures may include and/or may bebased on, for example, Received Signal Strength Indicator (“RSSI”)values, Reference Signal Receive Power (“RSRP”) values,Signal-to-Interference-and-Noise-Ratio (“SINR”) values, Channel QualityIndicator (“CQI”) values, or the like.

In some embodiments, such measures may be included in, and/or derivedfrom, measurement reports received from UEs located within sectors 101.For example, a particular measurement report from a given UE mayindicate that the UE detected RF signals, according to a particular setof frequencies, from a first base station 103-1, and further that the UEdetected RF signals, according to the same set of frequencies, from asecond base station 103-2. In this scenario, the detection of RF signalsaccording to the same set of frequencies from two different base station(i.e., base station 103-1 and 103-2) may indicate RF interference at ageographical location of the UE, arising from signals output by basestations 103-1 and 103-2.

As another example, the measurement report from a given UE may indicatea relatively low RSSI value, RSRP value, etc. associated with signalsfrom a given base station 103. Such values may be “relatively” low inthat such values may be below a threshold value, and/or may be lowerthan an expected value (e.g., which may be determined based on ahistorical analysis of RF metrics). Further, such analysis may beperformed based on location, where a first threshold value may be usedat a first location (e.g., relatively close to base station 103), whilea second threshold value may be used at a second location (e.g., fartheraway from base station 103).

In some embodiments, GOS 109 may further receive and/or maintainattribute and/or characteristic information for one or more sectors 101.Briefly, such attribute and/or characteristic information may includeconfiguration parameters (e.g., beamforming configuration parameters, RFtransmission power parameters, Multiple-Input Multiple-Output (“MIMO”)configuration parameters, or the like), physical network infrastructureparameters (e.g., antenna height, antenna location, etc.), localefeatures (e.g., building density, topographical information, or thelike), and/or other types of information associated with respectivesectors 101 and/or network infrastructure associated with respectivesectors 101 (e.g., network infrastructure located within given sectors101, and/or providing wireless service to given sectors 101).

In some embodiments, GOS 109 may communicate with base stations 103 ofsectors 101 and/or UEs located within such sectors 101 via anapplication programming interface (“API”), an X2 interface, and/or someother suitable communication pathway, in order to receive suchinformation. For example, base stations 103 and/or UEs communicativelycoupled to respective base stations 103 may “push” such information toGOS 109 (e.g., via the API) on a periodic or intermittent basis, uponthe occurrence of trigger events (e.g., the detection of a referencesignal from one or more base stations 103 by a UE located within a givensector 101, one or more Quality of Service (“QoS”) metrics exceeding athreshold value, a connection or disconnection of one or more UEs to oneor more base stations 103, and/or other events), and/or on some otherbasis. In some embodiments, GOS 109 may “pull” (e.g., request orotherwise obtain) such information from the UEs, base stations 103,and/or other device or system that receives, collects, maintains, and/orprovides such information. For example, GOS 109 may be communicativelycoupled to a Service Capability Exposure Function (“SCEF”) of a corenetwork associated with base stations 103, a Network Exposure Function(“NEF”), and/or other suitable device, system, function, etc.

As further shown, GOS 109 may determine (at 104) one or more sectormodels associated with respective sectors 101 based on the received (at102) KPIs, attributes, etc. For example, as discussed below, GOS 109 mayuse AI/ML techniques or other suitable techniques to identify one ormore sector models that include attributes, KPIs, etc. that are similarto the attributes, KPIs, etc. associated with respective sectors 101.For example, when determining whether attributes of a given sector modelare “similar” to attributes of a given sector 101, GOS 109 may generateone or more scores, classifiers, or the like, and/or may perform asuitable similarity analysis to determine a measure of similaritybetween attributes of a set of sector models and attributes of a givensector 101. In some embodiments, GOS 109 may select a particular sectormodel if the measure of similarity exceeds a threshold measure ofsimilarity. Additionally, or alternatively, GOS 109 may select aparticular quantity of highest-scoring sector models (e.g., the highestscoring sector model, the top three scoring sector models, etc.). Insome embodiments, GOS 109 may select a particular quantity ofhighest-scoring sector models, so long as the scores associated withsuch sector models exceed a threshold score (e.g., the top three scoringsector models so long as the top three scoring sector models exceed thethreshold score, the top two scoring sector models if the thirdhighest-scoring sector model is below the threshold score, etc.).

GOS 109 may further receive (at 106) location information and/orattributes associated with MRU 105. For example, GOS 109 may receivelocation information associated with MRU 105 after MRU 105 is deployedwithin a given sector (e.g., sector 101-3). For example, MRU 105 mayoutput location information to GOS 109 after MRU 105 has been stationaryfor at least a threshold amount of time (e.g., indicating that MRU 105has completed traveling from an origin to a destination within sector101-3). As another example, MRU 105 may output location information toGOS 109 after a vehicle or other type of mobility apparatus associatedwith MRU 105, and/or to which MRU 105 is affixed, has been powered off.In some embodiments, GOS 109 may receive an estimated and/or predictedlocation of MRU 105 (e.g., within sector 101-3) prior to GOS 109 beinglocated within sector 101-3. As yet another example, GOS 109 mayevaluate different locations within region 100 (e.g., within differentsectors 101, and/or at particular locations within sectors 101) in orderto determine optimal configuration parameters for different locations.In such scenarios, after MRU 105 has been deployed to a particularlocation, optimal configuration parameters may be provided to MRU 105and/or to suitable sectors 101 in a manner described below.

GOS 109 may additionally receive (at 106) attributes associated with MRU105. For example, GOS 109 may receive configuration parameters (e.g.,beamforming configuration parameters, RF transmission power parameters,MIMO configuration parameters, bands and/or frequencies implemented byMRU 105, or the like), physical network infrastructure parameters (e.g.,antenna height, antenna location, etc.), and/or other types ofinformation associated with a configuration of MRU 105 and/or networkinfrastructure associated with MRU 105. Additionally, or alternatively,GOS 109 may receive performance metrics associated with MRU 105, such asan amount of measured latency, throughput, jitter, etc. of trafficbetween MRU 105 and one or more UEs. Additionally, or alternatively, GOS109 may receive performance and/or other information regarding one ormore MECs implemented by MRU 105, such as a measure of processingresources and/or capacity, a measure of memory resources and/orcapacity, a measure of storage resources, or the like. In someembodiments, GOS 109 may receive information indicating which parametersof MRU 105 are static and which parameters are configurable. Forexample, MRU 105 may be associated with some parameters which may not bechanged and/or which are not authorized to be changed, while otherparameters may be able to be changed and/or are authorized to bechanged. GOS 109 may receive (at 106) information regarding MRU 105 fromMRU 105 (e.g., via an API or other suitable communication pathway)and/or from some other device or system that maintains and/or providessuch information regarding MRU 105.

GOS 109 may further determine (at 108) an MRU model based on theattributes of MRU 105. For example, as discussed below, GOS 109 may useAI/ML techniques or other suitable techniques to identify one or moreMRU models that include attributes, KPIs, etc. that are similar to theattributes, KPIs, etc. associated with MRU 105. For example, whendetermining whether attributes of a given MRU model are “similar” toattributes of MRU 105, GOS 109 may generate one or more scores,classifiers, or the like, and/or may perform a suitable similarityanalysis to determine a measure of similarity between attributes of aset of MRU models and attributes of MRU 105. In some embodiments, GOS109 may select a particular MRU model if the measure of similarityexceeds a threshold measure of similarity. Additionally, oralternatively, GOS 109 may select a particular quantity ofhighest-scoring MRU models (e.g., the highest scoring MRU model, the topthree scoring MRU models, etc.). In some embodiments, GOS 109 may selecta particular quantity of highest-scoring MRU models, so long as thescores associated with such MRU models exceed a threshold score (e.g.,the top three scoring MRU models so long as the top three scoring MRUmodels exceed the threshold score, the top two scoring MRU models if thethird highest-scoring MRU model is below the threshold score, etc.).

As shown in FIG. 1B, GOS 109 may further determine (at 110) one or moreconfiguration parameters, remedial actions, and/or performance-enhancingactions to implement with respect to one or more sectors 101 and/or MRU105. GOS 109 may further provide (at 112 and 114, respectively) theconfiguration parameters to MRU 105 and one or more sectors 101 (e.g.,base stations 103 of one or more sectors 101). MRU 105 and sectors 101may perform actions and/or modify configuration parameters based on thereceived (at 112 and 114) configuration parameters.

For example, GOS 109 may determine actions such as modifying PhysicalResource Blocks (“PRBs”) used by MRU 105 and/or one or more basestations 103, in order to avoid collisions or interference betweenwireless signals associated with MRU 105 and the one or more basestations 103. In some embodiments, the actions may include one or moreother types of actions, such as modifying a beamforming configuration ofMRU 105 and/or one or more base stations 103 (e.g., beam width, power,azimuth angle, and/or tilt angle), implementing or modifying a cellsuspend mode at one or more base stations 103, implementing or modifyinga coordinated multi-point configuration of MRU 105 and/or multiple basestations 103 (e.g., base stations 103 that interfere with each other orwith MRU 105), and/or other suitable actions.

In some embodiments, the configuration parameters, actions, etc. mayinclude mobility parameters, such as modifying an NCL associated withone or more base stations 103 to include MRU 105. Additionally, oralternatively, the configuration parameters may include modifying apriority or ranking of an NCL or other suitable information, to identifyMRU 105 as a “preferred” cell for handovers. For example, suchmodification may cause base stations 103 to facilitate handovers of UEsto MRU 105 in appropriate situations, such as when UEs are within acoverage area of MRU 105 and/or when signal quality metrics associatedwith MRU 105 and the UEs exceed a threshold level.

As additionally noted above, the configuration parameters, actions, etc.may include modifying traffic routing parameters, such as causing one ormore base stations 103 to route traffic to MRU 105. Such traffic mayinclude, for example, traffic that is to be processed by a MEC. Forexample, a MEC associated with a given base station 103 may beoverloaded (e.g., processing resource utilization associated with theMEC may be above a threshold measure of utilization), and routing suchtraffic to a MEC associated with MRU 105 may reduce the load on the MECassociated with base station 103, thus resulting in improved overallperformance of MEC traffic.

As another example, modifying traffic routing parameters may includecausing MRU 105 to be used as a communication relay associated with oneor more base stations 103. For example, MRU 105 may be configured as a“network extender” or communication relay for a given base station 103.Such a scenario may occur when one or more sectors 101 in which MRU 105is located (or is expected to be located) has gaps in wireless coverageprovided by base stations 103, where such “gaps” may refer to areas inwhich no wireless coverage is provided, and/or in which wirelesscoverage is provided with signal strength or quality (or other suitablemetrics) below one or more threshold values. MRU 105 may thus connect toa given base station 103 (e.g., within the same sector 101 as MRU 105and/or within a sector 101 that is proximate to the sector 101 in whichMRU 105 is located) in an “extender” or “repeater” configuration, inwhich MRU 105 provides wireless coverage to UEs within a coverage areaof MRU 105 and relays traffic between the UEs and base station 103.

As yet another example, modifying traffic routing parameters may includecausing MRU 105 to be used as a wireless backhaul link between one ormore base stations 103 and a core network. For example, MRU 105 may beconfigured as an Integrated Access Backhaul (“TAB”) node or some othertype of suitable configuration, in which MRU 105 serves as acommunication relay between one or more base stations 103 and the corenetwork. For example, MRU 105 may receive traffic from the core networkfor one or more UEs, and may provide the traffic over a wirelessinterface to a particular base station 103 to which the UE is connected.Similarly, MRU 105 may wirelessly receive traffic from one or more UEsvia one or more base stations 103, and may forward the traffic to thecore network.

In some embodiments, some of the configuration parameters may bemodified on a per-slice or other granular or segmented basis. Forexample, the wireless network may be associated with different “slices,”Data Network Names (“DNNs”), QoS flows, or other delineations, where aslice (or other delineation) refers to a differentiated set of traffictreatment, QoS levels, Service Level Agreements (“SLAs”), or the like.For example, assume that a given base station 103 associated with aparticular sector 101 carries traffic associated with two differentslices having two different QoS levels. In the event that latencyassociated with a first slice (but not a second slice) is above athreshold level, GOS 109 may determine (at 110) configuration parametersin order to improve the latency of traffic associated with the firstslice. Such configuration parameters may omit any modifications totreatment or handling of the second slice.

For example, GOS 109 may determine (at 110) that MRU 105 should act as arelay node between UEs and base station 103 for traffic associated withthe first slice, but may not determine that MRU 105 should act as arelay node between UEs and base station for traffic associated with thesecond slice. In some embodiments, additional or alternativeclassifications, categories, attributes, or the like may be used todifferentially treat traffic and/or to configure MRU 105 and/or sectors101. For example, GOS 109 may configure sectors 101 to route trafficassociated with particular applications and/or particular sets of UEs(e.g., UEs associated with a particular organization, group, enterprise,device type, etc.) to a MEC associated with MRU 105, while trafficassociated with different applications and/or particular sets of UEs maybe handled by MECs associated with base stations 103 located withinsectors 101 (and/or may be provided to a core network).

The configuration (at 110 and/or 112) of MRU 105 and/or sectors 101 mayallow for “touchless” configuration of MRU 105 and/or sectors 101 basedon the presence of MRU 105 within a particular sector 101. For example,an administrator or other entity may not need to manually configureparameters of MRU 105 and/or sectors 101 in order to achieve an optimalconfiguration of MRU 105 and/or sectors 101, or even to determine whatconfiguration parameters would result in optimal performance. Forexample, embodiments described herein may automatically determinepotential shortcomings at a particular location within a given sector101 (e.g., high latency at the location, low throughput at the location,slow MEC processing at the location, high RF interference at thelocation, etc.), and may configure MRU 105 to remediate suchshortcomings (e.g., where such remediation may further be based onattributes or capabilities of MRU 105). Further, sectors 101 may beconfigured in conjunction with MRU 105, such that the configuration ofMRU 105 and sectors 101, as a whole, complement each other and bothserve to achieve an overall improvement in network performance.

FIG. 2 illustrates example MRU models, sector models, and/oractions/parameters that may be generated, received, maintained,provided, etc. by GOS 109. For example, GOS 109 may be associated with aset of sector models 201, such as example sector models 201-1, 201-2,and 201-M. Further, GOS 109 may be associated with a set of MRU models203, such as example MRU models 203-1, 203-2, and 203-L. Additionally,GOS 109 may be associated with a set of actions/parameters 205, such asexample actions/parameters 205-1, 205-2, and 205-N.

GOS 109 may generate and/or modify sector models 201, MRU models 203,and/or actions/parameters 205 based on AI/ML techniques or othersuitable techniques. For example, GOS 109 may generate, modify, refine,etc. sector models 201, MRU models 203, and/or actions/parameters 205based on an evaluation of real-world data from sectors 101 and/orsimulations of RF and/or interference metrics in a simulation and/ortest environment. GOS 109 may further determine or identify correlationsbetween respective sector models 201, MRU models 203, and/oractions/parameters 205 using AI/ML techniques or other suitabletechniques.

For example, as shown in FIG. 3 , sector model 201 (e.g., associatedwith a given sector 101) may include RF and/or interference metrics 301(referred to simply as “RF metrics 301” for the sake of brevity), QoSmetrics 303, energy consumption metrics 305, RAN configurationparameters 307, inter-sector information 309, locale features 311,and/or one or more other types of information.

RF metrics 301 associated with a given sector 101 may include metricsrelated to the propagation of RF signals from network infrastructurewithin sector 101 (or providing service to sector 101). For example, RFmetrics 301 may include RSSI values, RSRP values, SINR values, CQIvalues, or other indicators of RF signal quality or strength. In someembodiments, RF metrics 301 may be determined by UEs or other RFsignal-receiving devices located within or near (e.g., within athreshold distance of) sector 101.

For example, a given UE may be configured to scan for the presence of RFsignals, such as reference signals, emitted by one or more base stations103. The UE may, in some embodiments, generate one or more measurementreports, which may indicate a signal strength of such transmissionsreceived from one or more base stations 103, and may further include anidentifier of the base station(s) 103 from which such transmissions werereceived. The measurement reports may further include informationregarding the transmissions themselves, such as a frequency (or range offrequencies) on which the RF signals were detected by the UE, as well astiming information (e.g., timing offsets, frame, time slot, etc.)associated with the RF signals. In this manner, the PRBs associated withthe received RF signals may be indicated or derived from the measurementreports, where a particular PRB refers to a particulartime-and-frequency slot in a time-and-frequency domain. A UE may also beconfigured to determine or report measures of interference, such asSINR, received RF signal power (e.g., at particular frequencies and/ortime slots), or other measures of interference.

As noted above, GOS 109 may receive the measurement reports and/or othersuitable RF metrics 301 from UEs (e.g., via an API or other suitablecommunication pathway), and/or from base stations 103 (e.g., via an X2interface, Xn interface, or other suitable communication pathway, wherebase stations 103 may receive measurement reports from UEs via RadioResource Control (“RRC”) messaging or some other suitable communicationpathway). In some embodiments, GOS 109 may receive RF metrics 301 fromsome other device or system.

Based on the received RF metrics 301, GOS 109 may determine a “baseline”or “expected” level of interference, received power, and/or other RFmetrics in sector 101. As discussed below, such “baseline” or “expected”levels may be determined on a granular basis (e.g., based on locationsor regions within given sectors 101, such as regions that are delineatedby distance and angle from a reference point). Further, the “baseline”or “expected” RF or interference levels may be determined on a temporalbasis, which may reflect fluctuations that vary on a periodic,repeating, or otherwise determinable basis (e.g., fluctuations based onweekday commutes, seasonal traffic, events at venues, or otherphenomena). In this manner, sector model 201 may be determined for agiven sector 101 based on temporal and/or spatial aspects of RF metrics301. Additionally, or alternatively, different sector models 201 may bedetermined for sector 101 (e.g., for different times or time periods,and/or for different locations or regions within sector 101).

QoS metrics 303 may reflect QoS metrics associated with a particularsector 101 over a particular period of time. For example, QoS metrics303 may include metrics relating to latency, bandwidth, jitter, packetloss, and/or other metrics related to network layer performance,application layer performance, or other “higher” layer performance(e.g., performance at a layer above a physical layer and/or a data linklayer). QoS metrics 303 associated with a given sector 101 may becollected from and/or reported by UEs receiving wireless service withinsector 101 and/or from a base station 103 located within sector 101,and/or may be received from base station 103 located in or providingwireless service to sector 101.

Energy consumption metrics 305 may indicate an amount of energy consumedat the particular sector 101 over the particular period of time. Forexample, energy consumption metrics 305 may indicate an amount ofelectrical power (e.g., kilowatt-hours or some other measure of consumedpower) consumed by network infrastructure elements (e.g., base stations103, data centers, routers, hubs, and/or other equipment) within orserving sector 101 over a given period of time.

RAN configuration parameters 307 may include parameters such as anindication of quantity and/or position (e.g., geographical position) ofphysical infrastructure hardware (e.g., antennas, radios, data centers,or the like) associated with one or more RANs in sector 101. In someembodiments, RAN configuration parameters 307 may indicate particularradio access technologies (“RATs”) implemented in sector 101 (e.g., aLTE RAT, a 5G RAT, etc.), beam configurations implemented in sector 101(e.g., beam quantity, beam azimuth angles, beam width, beam transmissionpower, etc.), antenna sensitivity (e.g., receive sensitivity), MIMOconfiguration information, and/or other suitable information. In someembodiments, RAN configuration parameters 307 may indicate the height ofone or more antennas associated with one or more base stations 103 (orother RF-emitting equipment), a coverage area of one or more antennas(e.g., a polygon, distance from a reference point, or other descriptorof geographical regions in which RF signals are received from the one ormore antennas), or other parameters of the one or more antennas.

In some embodiments, RAN configuration parameters 307 may includeinformation indicating a capacity or other capability of a given sector101 and/or one or more base stations 103 located in (or providingservice to) sector 101. For example, RAN configuration parameters 307may indicate an RF channel bandwidth within sector 101, an amount ofavailable and/or used PRBs associated with one or more base stations 103located in (or providing service to) sector 101, threshold quantities ofsupported UEs simultaneously connected to one or more base stations 103located in (or providing service to) sector 101, threshold amounts ofdata or throughput that may be sent and/or received by one or more basestations 103 located in (or providing service to) sector 101, and/orother capability and/or capacity-related information.

Inter-sector information 309 may include information associated withsectors adjacent to or proximate to a given sector 101. For example,inter-sector information 309 may include RF metrics, RAN parameters, QoSmetrics, and/or energy consumption metrics, associated with sectorsadjacent to or within a threshold distance of sector 101. In someembodiments, inter-sector information 309 may include mobilityinformation, which may be associated with mobility of UEs between sector101 and neighboring sectors. For example, inter-sector information 309may indicate that UEs that are located in sector 101 are likely to bestationary within sector 101 for a first duration of time (e.g.,approximately one hour), and then that such UEs travel to a particularneighboring sector. As another example, inter-sector information 309 mayindicate that UEs that are located in the neighboring sector arerelatively likely to enter the particular sector 101.

Locale features 311 may include information indicating attributes and/orfeatures of the geographical area. For example, locale features 311 mayinclude information relating to building layout and/or density,topographical features (e.g., mountains, valleys, forests, streams,etc.), weather-related information, air quality-related information(e.g., smog density, particulate density, fog density, etc.), and/orother factors that may affect RF metrics, energy consumption metrics,QoS metrics, or other metrics. Locale features 311 may includegeographical coordinates (e.g., latitude and longitude coordinates,Global Positioning System (“GPS”) coordinates, or the like) or othersuitable location information, to indicate the geographical locations ofrespective features.

As described below, a given sector 101 may be associated with one ormore sector models 201 based on a comparison of the above-describedfactors, and/or one or more other factors, of sector 101 to such factorsassociated with a set of candidate sector models 201. Briefly, forexample, GOS 109 may determine that a particular sector 101, thatexhibits a particular set of RF metrics 301, a particular set of QoSmetrics 303, a particular set of energy consumption metrics 305, and afirst set of locale features 311 (e.g., urban features such as high-risebuildings) is associated with a first sector model 201, while anothersector 101, that exhibits a similar set of RF metrics 301, a similar setof QoS metrics 303, and a similar set of energy consumption metrics 305,but a different second set of locale features 311 (e.g., rural featuressuch as relatively flat areas with relatively low building density) isassociated with a different second sector model 201.

Generally, a given sector model 201 may describe or reflect parameters,metrics, attributes, etc. of a given sector 101. As noted above, aparticular sector model 201 may include temporal or location-basedfluctuations or variations, and/or different sector models 201 may beassociated with a given sector 101 at different times or regions withinsector 101.

MRU models 203 may indicate attributes, characteristics, capabilityinformation, etc. associated with one or more MRUs 105. For example,different MRUs 105 may have different types of hardware (e.g., RANinfrastructure hardware, such as antennas; processing hardware; and/orother types of hardware resources). As another example, MRUs 105 mayhave different configuration parameters available at differing times ofday or based on other temporal factors. For example, a given MRU 105 maybe configured to implement, or make available, wireless coverageassociated with a particular set of bands during a first time period,while MRU 105 may be configured to not make the particular set of bandsavailable during a second time period. As another example, a given MRU105 may be may be configured to implement, or make available, wirelesscoverage associated with a particular set of bands when MRU 105 islocated at a first location, while MRU 105 may be configured to not makethe particular set of bands available at a second location (e.g., nearan airport, near a law enforcement facility, etc.). As such, differentMRUs 105 may be associated with different MRU models 203, and/or thesame MRU 105 may be associated with different MRU models 203 (e.g., atdifferent times, at different locations, etc.).

Similar to sector models 201, MRU model 203 may include and/or may bebased on RF metrics 301, QoS metrics 303, and/or energy consumptionmetrics 307. For example, as similarly discussed above, such information301-307, associated with a given MRU model 203, may indicate and/or maybe based on measured, simulated, and/or configured values associatedwith a given MRU 105. Further, MRU model 203 may include MECconfiguration parameters 313, which may indicate, for example, a loadand/or capacity associated with one or more MECs with which a given MRU105 is associated. For example, MEC configuration parameters mayindicate an amount of MEC processing resources that are used and/oravailable at MRU 105, an amount of MEC memory resources that are usedand/or available at MRU 105, an amount of MEC storage resources that areused and/or available at MRU 105, a set of applications and/or servicesthat are installed or that may be installed at a MEC associated with MRU105, and/or other MEC configuration or performance information.

In some embodiments, GOS 109 may determine a set of actions/parameters205, which may include actions/parameters 315 for one or more sectors101 and a set of actions/parameters 317 for MRU 105, based on respectivesector models 201, MRU model 203, and a location of MRU 105 (e.g.,within a given sector 101). That is, one or more sets of sectoractions/parameters 315 and MRU actions/parameters 317 may be associatedwith a set of sector models 201, MRU models 203, and MRU location. Thus,when MRU 105 is located at a particular location (e.g., within aparticular sector 101), configuration parameters for MRU 105 and one ormore base stations 103 may be associated with sector models 201associated with the particular sector 101 in which MRU 105 is located,and with one or more other sectors 101 (e.g., sectors 101 that areadjacent to or are otherwise proximate to the particular sector 101 inwhich MRU 105 is located).

For example, a set of sector models 203, associated with a set ofsectors 101, may indicate relatively poor RF metrics 301 at a particularlocation within a particular sector 101. Thus, sector actions/parameters315, associated with the given location within the particular sector 101(e.g., associated with sector models 203 and the location within sector101), may include actions/parameters such as configuring base stations103 in the particular sector 101 and/or surrounding sectors 101 toforward traffic to MRU 105 instead of directly to one or more UEs. Inother words, base stations 103 may be configured to use MRU 105 as arelay between base stations 103 and one or more UEs. Similarly, in thisexample, sector models 201 and MRU model 203 may be associated with MRUactions/parameters 317, which may include configuring MRU 105 to act asa relay between base stations 103 and one or more UEs.

As another example, sector models 203 may indicate relatively poor QoSmetrics 303 (e.g., latency above a threshold, throughput below athreshold, etc.) and relatively strong RF metrics 301 (e.g., RSSI abovea threshold, SINR above a threshold, etc.) at a given location. In thisexample, sector actions/parameters 315 may include configuring basestations 103 in a particular sector 101 and/or surrounding sectors 101to forward traffic to a core network via MRU 105 instead of via someother backhaul link. In other words, base stations 103 may be configuredto use MRU 105 as a wireless relay (e.g., as an IAB node) between basestations 103 and the core network. Similarly, in this example, sectormodels 201 and MRU model 203 may be associated with MRUactions/parameters 317, which may include configuring MRU 105 to act asa relay between base stations 103 and the core network.

As yet another example, sector models 203 may indicate that a particularlocation within a particular sector 101 is a “low mobility” location, inwhich UEs tend to remain for a relatively long time (e.g., at least athreshold amount time). In this example, sector actions/parameters 315may include configuring base stations 103 in the particular sector 101and/or surrounding sectors 101 to include MRU 105 in a NCL provided bybase stations 103, and/or to rank MRU 105 as a highest ranking neighborcell in the NCL (and/or to otherwise modify a ranking or score of MRU105 in the NCL) based on the “low mobility” determination associatedwith the location. Similarly, in this example, sector models 201 and MRUmodel 203 may be associated with MRU actions/parameters 317, which mayinclude configuring MRU 105 to serve as a base station of a RAN thatincludes base stations 103 of sectors 101.

As another example, sector models 203 may indicate that a particularlocation within a particular sector 101 is a location at which arelatively large amount of video streaming traffic is sent and/orreceived by UEs located at the location. In this example, sectoractions/parameters 315 may include configuring base stations 103 in theparticular sector 101 and/or surrounding sectors 101 to perform carrieraggregation and/or to modify beamforming parameters in order toimplement beams that do not overlap with the location, and/or where suchmodification reduces wireless coverage of base stations 103 at thelocation. Additionally, MRU actions/parameters 317 may includeconfiguring a beamforming configuration of MRU 105 to provide wirelesscoverage at the location. As a result, the beams implemented by basestations 103 and MRU 105 may be more tightly focused, thus providingenhanced coverage at respective coverage areas, including the particularlocation of MRU 105 as well as surrounding locations within sector 101(and/or surrounding sectors 101).

As a further example, sector actions/parameters 315 and/or MRUactions/parameters 317 may include a coordinated determination of one ormore frequencies, bands, etc. to utilize. For example, in someembodiments, sector actions/parameters 315 and MRU actions/parameters317 may include a configuration of base stations 103 and MRU 105 toutilize the same frequencies, bands, etc., such as in situations wherecoverage areas of base stations 103 and MRU 105 do not significantlyoverlap (e.g., as indicated by sector models 201 and MRU model 203). Asanother example, in situations where coverage areas of base stations 103and MRU overlap, base stations 103 and MRU 105 may be configured toutilize differing frequencies, bands, etc. (e.g., in order to reducecollisions, interference, etc.).

FIG. 4 illustrates an example determination of one or more sector models201 for a particular sector 101. As shown, GOS 109 may determine (at402) parameters and/or attributes of sector 101. As discussed above,such parameters and/or attributes may include RF metrics 301, QoSmetrics 303, energy consumption metrics 305, RAN configurationparameters 307, inter-sector information 309, locale features 311,and/or other suitable parameters, attributes, metrics, or the like. GOS109 may further identify (at 404) one or more sector models 201 based onthe determined parameters and/or attributes of sector 101.

In this example, GOS 109 may determine that sector 101 is associatedwith a “highway” sector model 401-1 and a “media streaming” sector model401-3. As further shown, GOS 109 may not determine that sector 101 isassociated with an example “office building” sector model 401-2, or anexample “dense buildings” sector model 401-4. For example, GOS 109 maydetermine, based on a suitable similarity analysis of the parametersand/or attributes of sector 101, that sector models 401-2 and 401-4 donot match (e.g., correspond with a measure of similarity above athreshold measure of similarity) sector 101, and/or that sector models401-1 and 401-3 match (e.g., have a higher measure of similarity with)the parameters and/or attributes of sector 101 more closely. Asdiscussed above, operations 402 and 404 may be performed on an ongoingbasis, such that the selection of particular sector models 401 maychange based on updated parameters and/or attributes received by GOS 109over time.

FIG. 5 illustrates an example process 500 for configuring elements of aRAN and an MRU based on sector models 201, MRU models 203, and alocation of a particular MRU 105. In some embodiments, some or all ofprocess 500 may be performed by GOS 109. In some embodiments, one ormore other devices may perform some or all of process 500 in concertwith, and/or in lieu of, GOS 109, such as one or more devices or systemsassociated with one or more sectors 101 and/or MRU 105.

As shown, process 500 may include generating, receiving, and/ormodifying (at 502) one or more sector models 201 based on metrics,parameters, etc. associated with one or more sectors 101 of a wirelessnetwork. For example, as discussed above, GOS 109 may use AI/MLtechniques or other suitable techniques to generate and/or refine sectormodels 201. For example, GOS 109 may evaluate metrics based on real-wordand/or simulated KPIs and/or attributes of one or more sectors 101 inorder to generate one or more clusters, classifications, or the like,which may be reflected by sector models 201.

Process 500 may further include generating, receiving, and/or modifying(at 504) one or more MRU models 203 associated with one or more MRUs105. For example, as discussed above, GOS 109 may use AI/ML techniquesor other suitable techniques to generate and/or refine MRU models 203.For example, GOS 109 may evaluate metrics based on real-word and/orsimulated KPIs and/or attributes of one or more MRUs 105 in order togenerate one or more clusters, classifications, or the like, which maybe reflected by MRU models 203.

Process 500 may additionally include determining (at 506) a particularsector model associated with a particular sector 101 of a RAN based onattributes of the particular sector 101. For example, as discussedabove, GOS 109 may receive information associated with sector 101, suchas RF metrics 301, QoS metrics 303, energy consumption metrics 305, RANconfiguration parameters 307, inter-sector information 309, localefeatures 311, and/or other suitable information, which GOS 109 maycompare to attributes of one or more sector models 201, in order todetermine one or more particular sector models 201 associated withsector 101. For example, as discussed above, GOS 109 may use one or moreAI/ML techniques to determine an association, correlation, or the likebetween the attributes associated with sector 101 and one or more sectormodels 201. For example, GOS 109 may select a particular sector model201 from a set of candidate sector models 201, and/or may generate a newsector model 201 based on the attributes of sector 101.

Process 500 may also include determining (at 508) a particular MRU modelassociated with a one or more MRUs 105. For example, as discussed above,GOS 109 may receive information associated with a given MRU 105, such asRF metrics 301, QoS metrics 303, energy consumption metrics 305, RANconfiguration parameters 307, MEC configuration parameters 313, and/orother suitable information, which GOS 109 may compare to attributes ofone or more sector models 201, in order to determine one or moreparticular MRU models 203 associated with MRU 105. For example, asdiscussed above, GOS 109 may use one or more AI/ML techniques todetermine an association, correlation, or the like between theattributes associated with MRU 105 and one or more MRU models 203. Forexample, GOS 109 may select a particular MRU model 203 from a set ofcandidate MRU models 203, and/or may generate a new MRU model 203 basedon the attributes of MRU 105.

Process 500 may additionally include identifying (at 510) a geographicallocation, within a particular sector 101, at which MRU 105 is located orwill be located. For example, GOS 109 may receive (e.g., from MRU 105,from a given base station 103 that is communications range of MRU 105,and/or some other device or system) that MRU 105 is located at aparticular geographical location within a given sector 105.Additionally, or alternatively, GOS 109 and/or some other device orsystem may utilize AI/ML techniques or other suitable techniques topredict (e.g., based on historical information associated with MRU 105and/or with sector 101) that MRU 105 will be located at the geographicallocation within sector 101 at a certain time or time range.

Process 500 may additionally include selecting (at 512) configurationparameters for MRU 105, sector 101 (e.g., the particular sector 101 inwhich MRU 105 is located or will be located), and/or one or more othersectors (e.g., sectors 101 adjacent to particular sector 101, sectors101 within a threshold proximity of particular sector 101, sectors 101within communications range of one or more base stations 103 ofparticular sector 101, and/or one or more other sectors 101). GOS 109may select such parameters 315 and/or 317 for sector(s) 101 and/or MRU105 further based on the geographical location at which MRU 105 islocated or will be located. For example, as discussed above, differingsets of actions/parameters 315 and/or 317 may be associated withdifferent sets of sector models 201, MRU models 203, and particulargeographical locations (e.g., within sectors 101) of MRU 105. In thismanner, the configurations of sectors 101 and MRU 105 may becomplementary and coordinated in a manner that optimizes the operationof a RAN implemented by base stations 103 of sectors 101 and MRU 105.

Process 500 may further include implementing (at 514) the identified setof actions. For example, as discussed above, sectors 101 may make one ormore configs or adjustments to parameters (e.g., frequency and/or bandselection parameters, MEC routing parameters, slice-specific parameters,NCL and/or other mobility parameters, backhaul parameters, and/or otherparameters), physical devices (e.g., antennas), or the like based on theidentified set of sector actions/parameters 315. Additionally, MRU 105may make one or more configs or adjustments to parameters (e.g.,frequency and/or band selection parameters, MEC routing parameters,slice-specific parameters, NCL and/or other mobility parameters,backhaul parameters, and/or other parameters), physical devices (e.g.,antennas), or the like based on the identified set of MRUactions/parameters 317.

As shown in FIG. 5 , some or all of process 500 may be performed and/orrepeated iteratively. For example, some or all of operations 502-514 maybe repeated and/or performed, in order to continuously remediatepotential RF interference within a given sector 101. That is, theresults of implementing (at 514) particular actions in response toparticular sector models 201 and MRU models 203 may be evaluated (e.g.,based on continued monitoring (at 502 and 504) or RAN and/or MRUmetrics). Further, the associations between sector models 201, MRU 203,and sets of actions/parameters 315 and/or 317 may be modified (e.g.,strengthened or weakened) based on whether particular actions/parameters315 and/or 317 improved performance, reliability, or the like withinsector 101 and/or surrounding sectors 101.

FIG. 6 illustrates an example environment 600, in which one or moreembodiments may be implemented. In some embodiments, environment 600 maycorrespond to a 5G network, and/or may include elements of a 5G network.In some embodiments, environment 600 may correspond to a 5GNon-Standalone (“NSA”) architecture, in which a 5G radio accesstechnology (“RAT”) may be used in conjunction with one or more otherRATs (e.g., an LTE RAT), and/or in which elements of a 5G core networkmay be implemented by, may be communicatively coupled with, and/or mayinclude elements of another type of core network (e.g., an evolvedpacket core (“EPC”)). As shown, environment 600 may include UE 601, RAN610 (which may include one or more Next Generation Node Bs (“gNBs”)611), RAN 612 (which may include one or more one or more evolved Node Bs(“eNBs”) 613), and various network functions such as Access and MobilityManagement Function (“AMF”) 615, Mobility Management Entity (“MME”) 616,Serving Gateway (“SGW”) 617, Session Management Function (“SMF”)/PacketData Network (“PDN”) Gateway (“PGW”)-Control plane function (“PGW-C”)620, Policy Control Function (“PCF”)/Policy Charging and Rules Function(“PCRF”) 625, Application Function (“AF”) 630, User Plane Function(“UPF”)/PGW-User plane function (“PGW-U”) 635, Home Subscriber Server(“HSS”)/Unified Data Management (“UDM”) 640, and Authentication ServerFunction (“AUSF”) 645. Environment 600 may also include one or morenetworks, such as Data Network (“DN”) 650. Environment 600 may includeone or more additional devices or systems communicatively coupled to oneor more networks (e.g., DN 650), such as GOS 109 and/or MRU 105, whichmay perform operations as described above.

The example shown in FIG. 6 illustrates one instance of each networkcomponent or function (e.g., one instance of SMF/PGW-C 620, PCF/PCRF625, UPF/PGW-U 635, HSS/UDM 640, and/or AUSF 645). In practice,environment 600 may include multiple instances of such components orfunctions. For example, in some embodiments, environment 600 may includemultiple “slices” of a core network, where each slice includes adiscrete set of network functions (e.g., one slice may include a firstinstance of SMF/PGW-C 620, PCF/PCRF 625, UPF/PGW-U 635, HSS/UDM 640,and/or AUSF 645, while another slice may include a second instance ofSMF/PGW-C 620, PCF/PCRF 625, UPF/PGW-U 635, HSS/UDM 640, and/or AUSF645). The different slices may provide differentiated levels of service,such as service in accordance with different Quality of Service (“QoS”)parameters.

The quantity of devices and/or networks, illustrated in FIG. 6 , isprovided for explanatory purposes only. In practice, environment 600 mayinclude additional devices and/or networks, fewer devices and/ornetworks, different devices and/or networks, or differently arrangeddevices and/or networks than illustrated in FIG. 6 . For example, whilenot shown, environment 600 may include devices that facilitate or enablecommunication between various components shown in environment 600, suchas routers, modems, gateways, switches, hubs, etc. Alternatively, oradditionally, one or more of the devices of environment 600 may performone or more network functions described as being performed by anotherone or more of the devices of environment 600. Devices of environment600 may interconnect with each other and/or other devices via wiredconnections, wireless connections, or a combination of wired andwireless connections. In some implementations, one or more devices ofenvironment 600 may be physically integrated in, and/or may bephysically attached to, one or more other devices of environment 600.

UE 601 may include a computation and communication device, such as awireless mobile communication device that is capable of communicatingwith RAN 610, RAN 612, and/or DN 650. UE 601 may be, or may include, aradiotelephone, a personal communications system (“PCS”) terminal (e.g.,a device that combines a cellular radiotelephone with data processingand data communications capabilities), a personal digital assistant(“PDA”) (e.g., a device that may include a radiotelephone, a pager,Internet/intranet access, etc.), a smart phone, a laptop computer, atablet computer, a camera, a personal gaming system, an IoT device(e.g., a sensor, a smart home appliance, or the like), a wearabledevice, an Internet of Things (“IoT”) device, a Machine-to-Machine(“M2M”) device, or another type of mobile computation and communicationdevice. UE 601 may send traffic to and/or receive traffic (e.g., userplane traffic) from DN 650 via RAN 610, RAN 612, and/or UPF/PGW-U 635.

RAN 610 may be, or may include, a 5G RAN that includes one or more basestations (e.g., one or more gNBs 611), via which UE 601 may communicatewith one or more other elements of environment 600. UE 601 maycommunicate with RAN 610 via an air interface (e.g., as provided by gNB611). For instance, RAN 610 may receive traffic (e.g., voice calltraffic, data traffic, messaging traffic, signaling traffic, etc.) fromUE 601 via the air interface, and may communicate the traffic toUPF/PGW-U 635, and/or one or more other devices or networks. Similarly,RAN 610 may receive traffic intended for UE 601 (e.g., from UPF/PGW-U635, AMF 615, and/or one or more other devices or networks) and maycommunicate the traffic to UE 601 via the air interface. In someembodiments, base station 103 and/or MRU 105 may be, may include, may beimplemented by, and/or may be communicatively coupled to one or moregNBs 611.

RAN 612 may be, or may include, a LTE RAN that includes one or more basestations (e.g., one or more eNBs 613), via which UE 601 may communicatewith one or more other elements of environment 600. UE 601 maycommunicate with RAN 612 via an air interface (e.g., as provided by eNB613). For instance, RAN 610 may receive traffic (e.g., voice calltraffic, data traffic, messaging traffic, signaling traffic, etc.) fromUE 601 via the air interface, and may communicate the traffic toUPF/PGW-U 635, and/or one or more other devices or networks. Similarly,RAN 610 may receive traffic intended for UE 601 (e.g., from UPF/PGW-U635, SGW 617, and/or one or more other devices or networks) and maycommunicate the traffic to UE 601 via the air interface. In someembodiments, base station 103 and/or MRU 105 may be, may include, may beimplemented by, and/or may be communicatively coupled to one or moreeNBs 613.

AMF 615 may include one or more devices, systems, Virtualized NetworkFunctions (“VNFs”), etc., that perform operations to register UE 601with the 5G network, to establish bearer channels associated with asession with UE 601, to hand off UE 601 from the 5G network to anothernetwork, to hand off UE 601 from the other network to the 5G network,manage mobility of UE 601 between RANs 610 and/or gNBs 611, and/or toperform other operations. In some embodiments, the 5G network mayinclude multiple AMFs 615, which communicate with each other via the N14interface (denoted in FIG. 6 by the line marked “N14” originating andterminating at AMF 615).

MME 616 may include one or more devices, systems, VNFs, etc., thatperform operations to register UE 601 with the EPC, to establish bearerchannels associated with a session with UE 601, to hand off UE 601 fromthe EPC to another network, to hand off UE 601 from another network tothe EPC, manage mobility of UE 601 between RANs 612 and/or eNBs 613,and/or to perform other operations.

SGW 617 may include one or more devices, systems, VNFs, etc., thataggregate traffic received from one or more eNBs 613 and send theaggregated traffic to an external network or device via UPF/PGW-U 635.Additionally, SGW 617 may aggregate traffic received from one or moreUPF/PGW-Us 635 and may send the aggregated traffic to one or more eNBs613. SGW 617 may operate as an anchor for the user plane duringinter-eNB handovers and as an anchor for mobility between differenttelecommunication networks or RANs (e.g., RANs 610 and 612).

SMF/PGW-C 620 may include one or more devices, systems, VNFs, etc., thatgather, process, store, and/or provide information in a manner describedherein. SMF/PGW-C 620 may, for example, facilitate the establishment ofcommunication sessions on behalf of UE 601. In some embodiments, theestablishment of communications sessions may be performed in accordancewith one or more policies provided by PCF/PCRF 625.

PCF/PCRF 625 may include one or more devices, systems, VNFs, etc., thataggregate information to and from the 5G network and/or other sources.PCF/PCRF 625 may receive information regarding policies and/orsubscriptions from one or more sources, such as subscriber databasesand/or from one or more users (such as, for example, an administratorassociated with PCF/PCRF 625).

AF 630 may include one or more devices, systems, VNFs, etc., thatreceive, store, and/or provide information that may be used indetermining parameters (e.g., quality of service parameters, chargingparameters, or the like) for certain applications.

UPF/PGW-U 635 may include one or more devices, systems, VNFs, etc., thatreceive, store, and/or provide data (e.g., user plane data). Forexample, UPF/PGW-U 635 may receive user plane data (e.g., voice calltraffic, data traffic, etc.), destined for UE 601, from DN 650, and mayforward the user plane data toward UE 601 (e.g., via RAN 610, SMF/PGW-C620, and/or one or more other devices). In some embodiments, multipleUPFs 635 may be deployed (e.g., in different geographical locations),and the delivery of content to UE 601 may be coordinated via the N9interface (e.g., as denoted in FIG. 6 by the line marked “N9”originating and terminating at UPF/PGW-U 635). Similarly, UPF/PGW-U 635may receive traffic from UE 601 (e.g., via RAN 610, SMF/PGW-C 620,and/or one or more other devices), and may forward the traffic toward DN650. In some embodiments, UPF/PGW-U 635 may communicate (e.g., via theN4 interface) with SMF/PGW-C 620, regarding user plane data processed byUPF/PGW-U 635.

HSS/UDM 640 and AUSF 645 may include one or more devices, systems, VNFs,etc., that manage, update, and/or store, in one or more memory devicesassociated with AUSF 645 and/or HSS/UDM 640, profile informationassociated with a subscriber. AUSF 645 and/or HSS/UDM 640 may performauthentication, authorization, and/or accounting operations associatedwith the subscriber and/or a communication session with UE 601.

DN 650 may include one or more wired and/or wireless networks. Forexample, DN 650 may include an Internet Protocol (“IP”)-based PDN, awide area network (“WAN”) such as the Internet, a private enterprisenetwork, and/or one or more other networks. UE 601 may communicate,through DN 650, with data servers, other UEs 601, and/or to otherservers or applications that are coupled to DN 650. DN 650 may beconnected to one or more other networks, such as a public switchedtelephone network (“PSTN”), a public land mobile network (“PLMN”),and/or another network. DN 650 may be connected to one or more devices,such as content providers, applications, web servers, and/or otherdevices, with which UE 601 may communicate.

FIG. 7 illustrates an example Distributed Unit (“DU”) network 700, whichmay be included in and/or implemented by one or more RANs (e.g., RAN610, RAN 612, or some other RAN) and/or MRU 105. In some embodiments, aparticular RAN may include one DU network 700. In some embodiments, aparticular RAN may include multiple DU networks 700. In someembodiments, DU network 700 may correspond to a particular gNB 611 of a5G RAN (e.g., RAN 610). In some embodiments, DU network 700 maycorrespond to multiple gNBs 611. In some embodiments, DU network 700 maycorrespond to one or more other types of base stations of one or moreother types of RANs. In some embodiments, MRU 105 may implement DUnetwork 700 and/or one or more elements thereof.

As shown, DU network 700 may include Central Unit (“CU”) 705, one ormore Distributed Units (“DUs”) 703-1 through 703-N (referred toindividually as “DU 703,” or collectively as “DUs 703”), and one or moreRadio Units (“RUs”) 701-1 through 701-M (referred to individually as “RU701,” or collectively as “RUs 701”).

CU 705 may communicate with a core of a wireless network (e.g., maycommunicate with one or more of the devices or systems described abovewith respect to FIG. 6 , such as AMF 615 and/or UPF/PGW-U 635). In theuplink direction (e.g., for traffic from UEs 601 to a core network), CU705 may aggregate traffic from DUs 703, and forward the aggregatedtraffic to the core network. In some embodiments, CU 705 may receivetraffic according to a given protocol (e.g., Radio Link Control (“RLC”))from DUs 703, and may perform higher-layer processing (e.g., mayaggregate/process RLC packets and generate Packet Data ConvergenceProtocol (“PDCP”) packets based on the RLC packets) on the trafficreceived from DUs 703.

In accordance with some embodiments, CU 705 may receive downlink traffic(e.g., traffic from the core network) for a particular UE 601, and maydetermine which DU(s) 703 should receive the downlink traffic. DU 703may include one or more devices that transmit traffic between a corenetwork (e.g., via CU 705) and UE 601 (e.g., via a respective RU 701).DU 703 may, for example, receive traffic from RU 701 at a first layer(e.g., physical (“PHY”) layer traffic, or lower PHY layer traffic), andmay process/aggregate the traffic to a second layer (e.g., upper PHYand/or RLC). DU 703 may receive traffic from CU 705 at the second layer,may process the traffic to the first layer, and provide the processedtraffic to a respective RU 701 for transmission to UE 601.

RU 701 may include hardware circuitry (e.g., one or more RFtransceivers, antennas, radios, and/or other suitable hardware) tocommunicate wirelessly (e.g., via an RF interface) with one or more UEs601, one or more other DUs 703 (e.g., via RUs 701 associated with DUs703), and/or any other suitable type of device. In the uplink direction,RU 701 may receive traffic from UE 601 and/or another DU 703 via the RFinterface and may provide the traffic to DU 703. In the downlinkdirection, RU 701 may receive traffic from DU 703, and may provide thetraffic to UE 601 and/or another DU 703.

RUs 701 may, in some embodiments, be communicatively coupled to one ormore MECs 707. For example, RU 701-1 may be communicatively coupled toMEC 707-1, RU 701-M may be communicatively coupled to MEC 707-M, DU703-1 may be communicatively coupled to MEC 707-2, DU 703-N may becommunicatively coupled to MEC 707-N, CU 705 may be communicativelycoupled to MEC 707-3, and so on. MECs 707 may include hardware resources(e.g., configurable or provisionable hardware resources) that may beconfigured to provide services and/or otherwise process traffic toand/or from UE 601, via a respective RU 701. In some embodiments, agiven MRU 105 may include and/or implement a particular RU 701 and/orone or more MECs 707.

In some embodiments, RU 701-1 may route some traffic, from UE 601, toMEC 707-1 instead of to a core network (e.g., via DU 703 and CU 705).MEC 707-1 may process the traffic, perform one or more computationsbased on the received traffic, and may provide traffic to UE 601 via RU701-1. In this manner, ultra-low latency services may be provided to UE601, as traffic does not need to traverse DU 703, CU 705, and anintervening backhaul network between DU network 700 and the corenetwork.

FIG. 8 illustrates an example O-RAN environment 800, which maycorrespond to RAN 610, RAN 612, and/or DU network 700. For example, RAN610, RAN 612, MRU 105, and/or DU network 700 may include one or moreinstances of O-RAN environment 800, and/or one or more instances ofO-RAN environment 800 may implement RAN 610, RAN 612, DU network 700,and/or some portion thereof. As shown, O-RAN environment 800 may includeNon-Real Time Radio Intelligent Controller (“RIC”) 801, Near-Real TimeRIC 803, O-eNB 805, O-CU-Control Plane (“O-CU-CP”) 807, O-CU-User Plane(“O-CU-UP”) 809, O-DU 811, O-RU 813, and O-Cloud 815. In someembodiments, O-RAN environment 800 may include additional, fewer,different, and/or differently arranged components.

In some embodiments, some or all of the elements of O-RAN environment800 may be implemented by one or more configurable or provisionableresources, such as virtual machines, cloud computing systems, physicalservers, and/or other types of configurable or provisionable resources.In some embodiments, some or all of O-RAN environment 800 may beimplemented by, and/or communicatively coupled to, one or more MECs 707.

Non-Real Time RIC 801 and Near-Real Time RIC 803 may receive performanceinformation (and/or other types of information) from one or moresources, and may configure other elements of O-RAN environment 800 basedon such performance or other information. For example, Near-Real TimeRIC 803 may receive performance information, via one or more E2interfaces, from O-eNB 805, O-CU-CP 807, and/or O-CU-UP 809, and maymodify parameters associated with O-eNB 805, O-CU-CP 807, and/or O-CU-UP809 based on such performance information. Similarly, Non-Real Time RIC801 may receive performance information associated with O-eNB 805,O-CU-CP 807, O-CU-UP 809, and/or one or more other elements of O-RANenvironment 800 and may utilize machine learning and/or other higherlevel computing or processing to determine modifications to theconfiguration of O-eNB 805, O-CU-CP 807, O-CU-UP 809, and/or otherelements of O-RAN environment 800. In some embodiments, Non-Real TimeRIC 801 may generate machine learning models based on performanceinformation associated with O-RAN environment 800 or other sources, andmay provide such models to Near-Real Time RIC 803 for implementation.

O-eNB 805 may perform functions similar to those described above withrespect to eNB 613. For example, O-eNB 805 may facilitate wirelesscommunications between UE 601 and a core network. O-CU-CP 807 mayperform control plane signaling to coordinate the aggregation and/ordistribution of traffic via one or more DUs 703, which may includeand/or be implemented by one or more O-DUs 811, and O-CU-UP 809 mayperform the aggregation and/or distribution of traffic via such DUs 703(e.g., O-DUs 811). O-DU 811 may be communicatively coupled to one ormore RUs 701, which may include and/or may be implemented by one or moreO-RUs 813. In some embodiments, O-Cloud 815 may include or beimplemented by one or more MECs 707, which may provide services, and maybe communicatively coupled, to O-CU-CP 807, O-CU-UP 809, O-DU 811,and/or O-RU 813 (e.g., via an O1 and/or O2 interface).

FIG. 9 illustrates example components of device 900. One or more of thedevices described above may include one or more devices 900. Device 900may include bus 910, processor 920, memory 930, input component 940,output component 950, and communication interface 960. In anotherimplementation, device 900 may include additional, fewer, different, ordifferently arranged components.

Bus 910 may include one or more communication paths that permitcommunication among the components of device 900. Processor 920 mayinclude a processor, microprocessor, or processing logic that mayinterpret and execute instructions. In some embodiments, processor 920may be or may include one or more hardware processors. Memory 930 mayinclude any type of dynamic storage device that may store informationand instructions for execution by processor 920, and/or any type ofnon-volatile storage device that may store information for use byprocessor 920.

Input component 940 may include a mechanism that permits an operator toinput information to device 900 and/or other receives or detects inputfrom a source external to 940, such as a touchpad, a touchscreen, akeyboard, a keypad, a button, a switch, a microphone or other audioinput component, etc. In some embodiments, input component 940 mayinclude, or may be communicatively coupled to, one or more sensors, suchas a motion sensor (e.g., which may be or may include a gyroscope,accelerometer, or the like), a location sensor (e.g., a GlobalPositioning System (“GPS”)-based location sensor or some other suitabletype of location sensor or location determination component), athermometer, a barometer, and/or some other type of sensor. Outputcomponent 950 may include a mechanism that outputs information to theoperator, such as a display, a speaker, one or more light emittingdiodes (“LEDs”), etc.

Communication interface 960 may include any transceiver-like mechanismthat enables device 900 to communicate with other devices and/orsystems. For example, communication interface 960 may include anEthernet interface, an optical interface, a coaxial interface, or thelike. Communication interface 960 may include a wireless communicationdevice, such as an infrared (“IR”) receiver, a Bluetooth® radio, or thelike. The wireless communication device may be coupled to an externaldevice, such as a remote control, a wireless keyboard, a mobiletelephone, etc. In some embodiments, device 900 may include more thanone communication interface 960. For instance, device 900 may include anoptical interface and an Ethernet interface.

Device 900 may perform certain operations relating to one or moreprocesses described above. Device 900 may perform these operations inresponse to processor 920 executing software instructions stored in acomputer-readable medium, such as memory 930. A computer-readable mediummay be defined as a non-transitory memory device. A memory device mayinclude space within a single physical memory 9 device or spread acrossmultiple physical memory devices. The software instructions may be readinto memory 930 from another computer-readable medium or from anotherdevice. The software instructions stored in memory 930 may causeprocessor 920 to perform processes described herein. Alternatively,hardwired circuitry may be used in place of or in combination withsoftware instructions to implement processes described herein. Thus,implementations described herein are not limited to any specificcombination of hardware circuitry and software.

The foregoing description of implementations provides illustration anddescription, but is not intended to be exhaustive or to limit thepossible implementations to the precise form disclosed. Modificationsand variations are possible in light of the above disclosure or may beacquired from practice of the implementations.

For example, while series of blocks and/or signals have been describedabove (e.g., with regard to FIGS. 1A, 1B, and 2-5 ), the order of theblocks and/or signals may be modified in other implementations. Further,non-dependent blocks and/or signals may be performed in parallel.Additionally, while the figures have been described in the context ofparticular devices performing particular acts, in practice, one or moreother devices may perform some or all of these acts in lieu of, or inaddition to, the above-mentioned devices.

The actual software code or specialized control hardware used toimplement an embodiment is not limiting of the embodiment. Thus, theoperation and behavior of the embodiment has been described withoutreference to the specific software code, it being understood thatsoftware and control hardware may be designed based on the descriptionherein.

In the preceding specification, various example embodiments have beendescribed with reference to the accompanying drawings. It will, however,be evident that various modifications and changes may be made thereto,and additional embodiments may be implemented, without departing fromthe broader scope of the invention as set forth in the claims thatfollow. The specification and drawings are accordingly to be regarded inan illustrative rather than restrictive sense.

Even though particular combinations of features are recited in theclaims and/or disclosed in the specification, these combinations are notintended to limit the disclosure of the possible implementations. Infact, many of these features may be combined in ways not specificallyrecited in the claims and/or disclosed in the specification. Althougheach dependent claim listed below may directly depend on only one otherclaim, the disclosure of the possible implementations includes eachdependent claim in combination with every other claim in the claim set.

Further, while certain connections or devices are shown, in practice,additional, fewer, or different, connections or devices may be used.Furthermore, while various devices and networks are shown separately, inpractice, the functionality of multiple devices may be performed by asingle device, or the functionality of one device may be performed bymultiple devices. Further, multiple ones of the illustrated networks maybe included in a single network, or a particular network may includemultiple networks. Further, while some devices are shown ascommunicating with a network, some such devices may be incorporated, inwhole or in part, as a part of the network.

To the extent the aforementioned implementations collect, store, oremploy personal information of individuals, groups or other entities, itshould be understood that such information shall be used in accordancewith all applicable laws concerning protection of personal information.Additionally, the collection, storage, and use of such information canbe subject to consent of the individual to such activity, for example,through well known “opt-in” or “opt-out” processes as can be appropriatefor the situation and type of information. Storage and use of personalinformation can be in an appropriately secure manner reflective of thetype of information, for example, through various access control,encryption and anonymization techniques for particularly sensitiveinformation.

No element, act, or instruction used in the present application shouldbe construed as critical or essential unless explicitly described assuch. An instance of the use of the term “and,” as used herein, does notnecessarily preclude the interpretation that the phrase “and/or” wasintended in that instance. Similarly, an instance of the use of the term“or,” as used herein, does not necessarily preclude the interpretationthat the phrase “and/or” was intended in that instance. Also, as usedherein, the article “a” is intended to include one or more items, andmay be used interchangeably with the phrase “one or more.” Where onlyone item is intended, the terms “one,” “single,” “only,” or similarlanguage is used. Further, the phrase “based on” is intended to mean“based, at least in part, on” unless explicitly stated otherwise.

What is claimed is:
 1. A device, comprising: one or more processorsconfigured to: generate a plurality of sector models, wherein eachsector model, of the plurality of sector models, is associated with arespective set of radio access network (“RAN”) attributes; generate aplurality of mobile radio unit (“MRU”) models, wherein each MRU model,of the plurality of MRU models, is associated with a respective set ofMRU attributes; identify a particular MRU, wherein the particular MRUprovides wireless connectivity to a core network, wherein the particularMRU includes a first Multi-Access/Mobile Edge Computing (“MEC”) deviceand mobility apparatus that facilitates mobility of the particular MRU,wherein identifying the particular MRU includes identifying a set ofattributes of the particular MRU; compare the attributes of theparticular MRU to MRU attributes included in the plurality of MRUmodels; select a particular MRU model, of the plurality of MRU models,based on the comparing; determine, based on historical informationassociated with the MRU, a predicted geographical location of the MRU ata particular time; identify, based on the predicted geographicallocation of the particular MRU, RAN attributes of a RAN that isassociated with the predicted geographical location and that provideswireless connectivity to the core network; select a particular sectormodel, of the plurality of sector models, based on the RAN attributes ofthe RAN associated with the predicted geographical location of theparticular MRU; determine, based on the particular MRU model and theparticular set of sector models: a first set of configuration parametersfor one or more base stations associated with the RAN associated withthe predicted geographical location of the MRU, wherein a particularbase station, of the one or more base stations, is associated with asecond MEC, and a second set of configuration parameters for theparticular MRU, wherein determining at least one of the first or secondsets of configuration parameters includes configuring the particularbase station to forward traffic to the particular MRU for processing bythe first MEC in lieu of processing the traffic at the second MECassociated with the particular base station; implement the first set ofconfiguration parameters at the one or more base stations of theparticular sector at the particular time; and implement the second setof configuration parameters at the particular MRU at the particulartime.
 2. The device of claim 1, wherein determining at least one of thefirst or second sets of configuration parameters further includes:determining, based on the set of sector models, that radio frequency(“RF”) metrics associated with the predicted geographical location ofthe particular MRU are below a threshold level of RF metrics; andconfiguring, based on determining that RF metrics associated with thepredicted geographical location of the particular MRU are below thethreshold level of RF metrics, the particular MRU as a wireless repeaterassociated with the one or more base stations.
 3. The device of claim 2,wherein configuring the particular MRU as the wireless repeaterincludes: configuring at least one base station, of the one or more basestations, to forward wireless traffic, associated with a UE, to theparticular MRU in lieu of forwarding the wireless traffic to the UE viawireless coverage provided by the at least one base station.
 4. Thedevice of claim 1, wherein determining at least one of the first orsecond sets of configuration parameters further includes: configuringthe particular MRU as a wireless backhaul device associated with the oneor more base stations.
 5. The device of claim 4, wherein configuring theparticular MRU as the wireless backhaul device includes: configuring atleast a particular base station, of the one or more base stations, toforward wireless traffic to the particular MRU in lieu of forwarding thewireless traffic to the core network.
 6. A non-transitorycomputer-readable medium, storing a plurality of processor-executableinstructions to: generate a plurality of sector models, wherein eachsector model, of the plurality of sector models, is associated with arespective set of radio access network (“RAN”) attributes; generate aplurality of mobile radio unit (“MRU”) models, wherein each MRU model,of the plurality of MRU models, is associated with a respective set ofMRU attributes; identify a particular MRU, wherein the particular MRUprovides wireless connectivity to a core network, wherein the particularMRU includes a first Multi-Access/Mobile Edge Computing (“MEC”) deviceand mobility apparatus that facilitates mobility of the particular MRU,wherein identifying the particular MRU includes identifying a set ofattributes of the particular MRU; compare the attributes of theparticular MRU to MRU attributes included in the plurality of MRUmodels; select a particular MRU model, of the plurality of MRU models,based on the comparing; determine, based on historical informationassociated with the MRU, a predicted geographical location of the MRU ata particular time; identify, based on the predicted geographicallocation of the particular MRU, RAN attributes of a RAN that isassociated with the predicted geographical location and that provideswireless connectivity to the core network; select a particular sectormodel, of the plurality of sector models, based on the RAN attributes ofthe RAN associated with the predicted geographical location of theparticular MRU; determine, based on the particular MRU model and theparticular set of sector models: a first set of configuration parametersfor one or more base stations associated with the RAN associated withthe predicted geographical location of the MRU, wherein a particularbase station, of the one or more base stations, is associated with asecond MEC, and a second set of configuration parameters for theparticular MRU, wherein determining at least one of the first or secondsets of configuration parameters includes configuring the particularbase station to forward traffic to the particular MRU for processing bythe first MEC in lieu of processing the traffic at the second MECassociated with the particular base station; implement the first set ofconfiguration parameters at the one or more base stations of theparticular sector at the particular time; and implement the second setof configuration parameters at the particular MRU at the particulartime.
 7. The non-transitory computer-readable medium of claim 6, whereindetermining at least one of the first or second sets of configurationparameters further includes: determining, based on the set of sectormodels, that radio frequency (“RF”) metrics associated with thepredicted geographical location of the particular MRU are below athreshold level of RF metrics; and configuring, based on determiningthat RF metrics associated with the predicted geographical location ofthe particular MRU are below the threshold level of RF metrics, theparticular MRU as a wireless repeater associated with the one or morebase stations.
 8. The non-transitory computer-readable medium of claim7, wherein configuring the particular MRU as the wireless repeaterincludes: configuring at least one base station, of the one or more basestations, to forward wireless traffic, associated with a UE, to theparticular MRU in lieu of forwarding the wireless traffic to the UE viawireless coverage provided by the at least one base station.
 9. Thenon-transitory computer-readable medium of claim 6, wherein determiningat least one of the first or second sets of configuration parametersfurther includes: configuring the particular MRU as a wireless backhauldevice associated with the one or more base stations.
 10. Thenon-transitory computer-readable medium of claim 9, wherein configuringthe particular MRU as the wireless backhaul device includes: configuringat least a particular base station, of the one or more base stations, toforward wireless traffic to the particular MRU in lieu of forwarding thewireless traffic to the core network.
 11. A method, comprising:generating a plurality of sector models, wherein each sector model, ofthe plurality of sector models, is associated with a respective set ofradio access network (“RAN”) attributes; generating a plurality ofmobile radio unit (“MRU”) models, wherein each MRU model, of theplurality of MRU models, is associated with a respective set of MRUattributes; identifying a particular MRU, wherein the particular MRUprovides wireless connectivity to a core network, wherein the particularMRU includes a first Multi-Access/Mobile Edge Computing (“MEC”) deviceand mobility apparatus that facilitates mobility of the particular MRU,wherein identifying the particular MRU includes identifying a set ofattributes of the particular MRU; comparing the attributes of theparticular MRU to MRU attributes included in the plurality of MRUmodels; selecting a particular MRU model, of the plurality of MRUmodels, based on the comparing; determining, based on historicalinformation associated with the MRU, a predicted geographical locationof the MRU at a particular time; identifying, based on the predictedgeographical location of the particular MRU, RAN attributes of a RANthat is associated with the predicted geographical location and thatprovides wireless connectivity to the core network; selecting aparticular sector model, of the plurality of sector models, based on theRAN attributes of the RAN associated with the predicted geographicallocation of the particular MRU; determining, based on the particular MRUmodel and the particular set of sector models: a first set ofconfiguration parameters for one or more base stations associated withthe RAN associated with the predicted geographical location of the MRU,wherein a particular base station, of the one or more base stations, isassociated with a second MEC, and a second set of configurationparameters for the particular MRU, wherein determining at least one ofthe first or second sets of configuration parameters includesconfiguring the particular base station to forward traffic to theparticular MRU for processing by the first MEC in lieu of processing thetraffic at the second MEC associated with the particular base station;implementing the first set of configuration parameters at the one ormore base stations of the particular sector at the particular time; andimplementing the second set of configuration parameters at theparticular MRU at the particular time.
 12. The method of claim 11,wherein determining at least one of the first or second sets ofconfiguration parameters further includes: determining, based on the setof sector models, that radio frequency (“RF”) metrics associated withthe predicted geographical location of the particular MRU are below athreshold level of RF metrics; and configuring, based on determiningthat RF metrics associated with the predicted geographical location ofthe particular MRU are below the threshold level of RF metrics, theparticular MRU as a wireless repeater associated with the one or morebase stations.
 13. The method of claim 12, wherein configuring theparticular MRU as the wireless repeater includes: configuring at leastone base station, of the one or more base stations, to forward wirelesstraffic, associated with a UE, to the particular MRU in lieu offorwarding the wireless traffic to the UE via wireless coverage providedby the at least one base station.
 14. The method of claim 11, whereindetermining at least one of the first or second sets of configurationparameters further includes: configuring the particular MRU as awireless backhaul device associated with the one or more base stations.15. The method of claim 14, wherein configuring the particular MRU asthe wireless backhaul device includes: configuring at least one basestation, of the one or more base stations, to forward wireless trafficto the particular MRU in lieu of forwarding the wireless traffic to thecore network.
 16. The method of claim 11, wherein selecting theparticular sector model includes selecting the particular sector modelfurther based on the particular time at which the MRU is predicted to belocated at the predicted geographical location.
 17. The device of claim1, wherein selecting the particular sector model includes selecting theparticular sector model further based on the particular time at whichthe MRU is predicted to be located at the predicted geographicallocation.
 18. The device of claim 1, wherein the plurality of MRU modelsinclude one or more artificial intelligence/machine learning (“AI/ML”)models.
 19. The non-transitory computer-readable medium of claim 6,wherein the plurality of MRU models include one or more artificialintelligence/machine learning (“AI/ML”) models.
 20. The method of claim11, wherein the plurality of MRU models include one or more artificialintelligence/machine learning (“AI/ML”) models.